Asthma symptoms, spirometry and air pollution exposure in schoolchildren in an informal settlement and an affluent area of Nairobi, Kenya

BACKGROUND

Although 1 billion people live in informal (slum) settlements, the consequences for respiratory health of living in these settlements remain largely unknown. This study investigated whether children living in an informal settlement in Nairobi, Kenya are at increased risk of asthma symptoms.

METHODS

Children attending schools in Mukuru (an informal settlement in Nairobi) and a more affluent area (Buruburu) were compared. Questionnaires quantified respiratory symptoms and environmental exposures; spirometry was performed; personal exposure to particulate matter (PM2.5) was estimated.

RESULTS

2373 children participated, 1277 in Mukuru (median age, IQR 11, 9-13 years, 53% girls), and 1096 in Buruburu (10, 8-12 years, 52% girls). Mukuru schoolchildren were from less affluent homes, had greater exposure to pollution sources and PM2.5. When compared with Buruburu schoolchildren, Mukuru schoolchildren had a greater prevalence of symptoms, 'current wheeze' (9.5% vs 6.4%, p=0.007) and 'trouble breathing' (16.3% vs 12.6%, p=0.01), and these symptoms were more severe and problematic. Diagnosed asthma was more common in Buruburu (2.8% vs 1.2%, p=0.004). Spirometry did not differ between Mukuru and Buruburu. Regardless of community, significant adverse associations were observed with self-reported exposure to 'vapours, dusts, gases, fumes', mosquito coil burning, adult smoker(s) in the home, refuse burning near homes and residential proximity to roads.

CONCLUSION

Children living in informal settlements are more likely to develop wheezing symptoms consistent with asthma that are more severe but less likely to be diagnosed as asthma. Self-reported but not objectively measured air pollution exposure was associated with increased risk of asthma symptoms.

Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept

Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as 'communities', reflecting co-occurrence patterns of chemicals) using a network analysis approach (n = 515 individuals) on 51 chemical substances in urine. The underlying question is whether the combined body burden of multiple chemicals is of potential health concern. If so, subsequent questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this, a biomonitoring hazard index was developed by summing over hazard quotients, where each biomarker concentration was weighted (divided) by the associated HBM health-based guidance value (HBM-HBGV, HBM value or equivalent). Altogether, for 17 out of the 51 substances, health-based guidance values were available. If the hazard index was higher than 1, then the community was considered of potential health concern and should be evaluated further. Overall, seven communities were identified in the GerES V data. Of the five mixture communities where a hazard index was calculated, the highest hazard community contained N-Acetyl-S-(2-carbamoyl-ethyl)cysteine (AAMA), but this was the only biomarker for which a guidance value was available. Of the other four communities, one included the phthalate metabolites mono-isobutyl phthalate (MiBP) and mono-n-butyl phthalate (MnBP) with high hazard quotients, which led to hazard indices that exceed the value of one in 5.8% of the participants included in the GerES V study. This biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies. Future mixture risk assessment using HBM data will benefit from additional HBM health-based guidance values based on population studies. Additionally, accounting for different biomonitoring matrices would provide a wider range of exposures. Future hazard index analyses could also take a common mode of action approach, rather than the more agnostic and non-specific approach we have taken in this proof of concept.

Measurement of SARS-CoV-2 in air and on surfaces in Scottish hospitals

BACKGROUND

There are still uncertainties in our knowledge of the amount of SARS-CoV-2 virus present in the environment - where it can be found, and potential exposure determinants - limiting our ability to effectively model and compare interventions for risk management.

AIM

This study measured SARS-CoV-2 in three hospitals in Scotland on surfaces and in air, alongside ventilation and patient care activities.

METHODS

Air sampling at 200 L/min for 20 min and surface sampling were performed in two wards designated to treat COVID-19-positive patients and two non-COVID-19 wards across three hospitals in November and December 2020.

FINDINGS

Detectable samples of SARS-CoV-2 were found in COVID-19 treatment wards but not in non-COVID-19 wards. Most samples were below assay detection limits, but maximum concentrations reached 1.7×10³ genomic copies/m³ in air and 1.9×10⁴ copies per surface swab (3.2×10² copies/cm² for surface loading). The estimated geometric mean air concentration (geometric standard deviation) across all hospitals was 0.41 (71) genomic copies/m³ and the corresponding values for surface contamination were 2.9 (29) copies/swab. SARS-CoV-2 RNA was found in non-patient areas (patient/visitor waiting rooms and personal protective equipment changing areas) associated with COVID-19 treatment wards.

CONCLUSION

Non-patient areas of the hospital may pose risks for infection transmission and further attention should be paid to these areas. Standardization of sampling methods will improve understanding of levels of environmental contamination. The pandemic has demonstrated a need to review and act upon the challenges of older hospital buildings meeting current ventilation guidance.

Multimedia exposures to arsenic and lead for children near an inactive mine tailings and smelter site

Children living near contaminated mining waste areas may have high exposures to metals from the environment. This study investigates whether exposure to arsenic and lead is higher in children in a community near a legacy mine and smelter site in Arizona compared to children in other parts of the United States and the relationship of that exposure to the site. Arsenic and lead were measured in residential soil, house dust, tap water, urine, and toenail samples from 70 children in 34 households up to 7 miles from the site. Soil and house dust were sieved, digested, and analyzed via ICP-MS. Tap water and urine were analyzed without digestion, while toenails were washed, digested and analyzed. Blood lead was analyzed by an independent, certified laboratory. Spearman correlation coefficients were calculated between each environmental media and urine and toenails for arsenic and lead. Geometric mean arsenic (standard deviation) concentrations for each matrix were: 22.1 (2.59) ppm and 12.4 (2.27)ppm for soil and house dust (<63μm), 5.71 (6.55)ppb for tap water, 14.0 (2.01)μg/L for specific gravity-corrected total urinary arsenic, 0.543 (3.22)ppm for toenails. Soil and vacuumed dust lead concentrations were 16.9 (2.03)ppm and 21.6 (1.90) ppm. The majority of blood lead levels were below the limit of quantification. Arsenic and lead concentrations in soil and house dust decreased with distance from the site. Concentrations in soil, house dust, tap water, along with floor dust loading were significantly associated with toenail and urinary arsenic but not lead. Mixed models showed that soil and tap water best predicted urinary arsenic. In our study, despite being present in mine tailings at similar levels, internal lead exposure was not high, but arsenic exposure was of concern, particularly from soil and tap water. Naturally occurring sources may be an additional important contributor to exposures in certain legacy mining areas.

Home Water Treatment Habits and Effectiveness in a Rural Arizona Community

Drinking water quality in the United States (US) is among the safest in the world. However, many residents, often in rural areas, rely on unregulated private wells or small municipal utilities for water needs. These utilities may violate the Safe Drinking Water Act contaminant guidelines, often because they lack the required financial resources. Residents may use alternative water sources or install a home water treatment system. Despite increased home water treatment adoption, few studies have examined their use and effectiveness in the US. Our study addresses this knowledge gap by examining home water treatment in a rural Arizona community. Water samples were analyzed for metal(loid)s, and home treatment and demographic data were recorded in 31 homes. Approximately 42% of homes treated their water. Independent of source water quality, residents with higher income (OR = 1.25; 95%CI (1.00 - 1.64)) and education levels (OR = 1.49; 95%CI (1.12 - 2.12)) were more likely to treat their water. Some contaminant concentrations were effectively reduced with treatment, while some were not. We conclude that increased educational outreach on contaminant testing and treatment, especially to rural areas with endemic water contamination, would result in a greater public health impact while reducing rural health disparities.

Intake fraction variability between air pollution emission sources inside an urban area

The cost-effective mitigation of adverse health effects caused by air pollution requires information on the contribution of different emission sources to exposure. In urban areas the exposure potential of different sources may vary significantly depending on emission height, population density, and other factors. In this study, we quantified this intraurban variability by predicting intake fraction (iF) for 3,066 emission sources in Warsaw, Poland. iF describes the fraction of the pollutant that is inhaled by people in the study area. We considered the following seven pollutants: particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), benzo[a] pyrene (BaP), nickel (Ni), cadmium (Cd), and lead (Pb). Emissions for these pollutants were grouped into four emission source categories (Mobile, Area, High Point, and Other Point sources). The dispersion of the pollutants was predicted with the CALPUFF dispersion model using the year 2005 emission rate data and meteorological records. The resulting annual average concentrations were combined with population data to predict the contribution of each individual source to population exposure. The iFs for different pollutant-source category combinations varied between 51 per million (PM from Mobile sources) and 0.013 per million (sulfate PM from High Point sources). The intraurban iF variability for Mobile sources primary PM emission was from 4 per million to 100 per million with the emission-weighted iF of 44 per million. These results propose that exposure due to intraurban air pollution emissions could be decreased more effectively by specifically targeting sources with high exposure potency rather than all sources.

Differences in metal concentration by particle size in house dust and soil

The majority of particles that adhere to hands are <63 μm in diameter yet risk assessments for soil remediation are typically based on soil samples sieved to <250 μm. The objective of our study was to determine if there is a significant difference in metal concentration by particle size in both house dust and soil. We obtained indoor dust and yard soil samples from 10 houses in Tucson, Arizona. All samples were sieved to <63 μm and 63 to <150 μm and analyzed for 30 elements via ICP-MS following nitric acid digestion. We conducted t-tests of the log-transformed data to assess for significant differences that were adjusted with a Bonferroni correction to account for multiple comparisons. In house dust, significant differences in concentration were observed for Be, Al, and Mo between particles sizes, with a higher concentration observed in the smaller particle sizes. Significant differences were also determined for Mg, Ca, Cr, Co, Cu, Ge, Zr, Ag, Ba, and Pb concentration in yard soil samples, with the higher concentration observed in the smaller particles size for each element. The results of this exploratory study indicate that current risk assessment practices for soil remediation may under estimate non-dietary ingestion exposure. This is of particular concern for young children who are more vulnerable to this exposure route due to their high hand mouthing frequencies. Additional studies with a greater number of samples and wider geographic distribution with different climates and soil types should be completed to determine the most relevant sampling practices for risk assessment.

Contribution to volatile organic compound exposures from time spent in stores and restaurants and bars

Many people spend time in stores and restaurants, yet there has been little investigation of the influence of these microenvironments on personal exposure. Relative to the outdoors, transportation, and the home, these microenvironments have high concentrations of several volatile organic compounds (VOCs). We developed a stochastic model to examine the effect of VOC concentrations in these microenvironments on total personal exposure for (1) non-smoking adults working in offices who spend time in stores and restaurants or bars and (2) non-smoking adults who work in these establishments. We also compared the effect of working in a smoking versus non-smoking restaurant or bar. Input concentrations for each microenvironment were developed from the literature whereas time activity inputs were taken from the National Human Activity Patterns Survey. Time-averaged exposures were simulated for 5000 individuals over a weeklong period for each analysis. Mean contributions to personal exposure from non-working time spent in stores and restaurants or bars range from <5% to 20%, depending on the VOC and time-activity patterns. At the 95th percentile of the distribution of the proportion of personal exposure attributable to time spent in stores and restaurants or bars, these microenvironments can be responsible for over half of a person's total exposure to certain VOCs. People working in restaurants or bars where smoking is allowed had the highest fraction of exposure attributable to their workplace. At the median, people who worked in stores or restaurants tended to have 20-60% of their total exposures from time spent at work. These results indicate that stores and restaurants can be large contributors to personal exposure to VOCs for both workers in those establishments and for a subset of people who visit these places, and that incorporation of these non-residential microenvironments can improve models of personal exposure distributions.

Ranking cancer risks of organic hazardous air pollutants in the United States

BACKGROUND

In this study we compared cancer risks from organic hazardous air pollutants (HAPs) based on total personal exposure summed across different microenvironments and exposure pathways.

METHODS

We developed distributions of personal exposure concentrations using field monitoring and modeling data for inhalation and, where relevant, ingestion pathways. We calculated risks for a nonoccupationally exposed and nonsmoking population using U.S. Environmental Protection Agency (EPA) and California Office of Environmental Health and Hazard Assessment (OEHHA) unit risks. We determined the contribution to risk from indoor versus outdoor sources using indoor/outdoor ratios for gaseous compounds and the infiltration factor for particle-bound compounds.

RESULTS

With OEHHA's unit risks, the highest ranking compounds based on the population median are 1,3-butadiene, formaldehyde, benzene, and dioxin, with risks on the order of 10(-4)-10(-5). The highest risk compounds with the U.S. EPA unit risks were dioxin, benzene, formaldehyde, and chloroform, with risks on a similar order of magnitude. Although indoor exposures are responsible for nearly 70% of risk using OEHHA's unit risks, when infiltration is accounted for, inhalation of outdoor sources contributed 50% to total risk, on average. Additionally, 15% of risk resulted from exposures through food, mainly due to dioxin.

CONCLUSIONS

Most of the polycyclic aromatic hydrocarbon, benzene, acetaldehyde, and 1,3-butadiene risk came from outdoor sources, whereas indoor sources were primarily responsible for chloroform, formaldehyde, and naphthalene risks. The infiltration of outdoor pollution into buildings, emissions from indoor sources, and uptake through food are all important to consider in reducing overall personal risk to HAPs.

Measured concentrations of VOCs in several non-residential microenvironments in the United States

Individuals spend about 25% of their time in non-residential indoor microenvironments. For some of these microenvironments, particularly stores and restaurants, exposures to volatile organic compounds (VOCs), have not been well characterized. In the Boston Exposure Assessment in Microenvironments (BEAM) study, sampling using scripted activities was conducted in stores, restaurants, and transportation in the summer of 2003 and winters of 2004 and 2005. A suite of VOCs including hydrocarbons, several chlorinated compounds, and aldehydes was analyzed. Nine store types were sampled using a composite design to enable a greater number of stores to be visited. Stores had higher concentrations of formaldehyde, toluene, ethylbenzene, xylenes, and styrene than other microenvironments, particularly in certain store types. Geometric mean formaldehyde levels were highest in the housewares stores, at 53 microg/m3 (95% CI = 43, 66). Geometric mean toluene levels were highest in multipurpose stores, at 76 microg/m3 (95% CI = 50, 118). The levels observed in stores were several times higherthan levels found in transportation microenvironments, and indicate strong indoor sources. In contrast, benzene did not have significantly higher levels in stores than typically found outdoors. Concentrations of formaldehyde and benzene, ethylbenzene, xylenes, and styrene were strongly influenced by the presence of smoking in the dining microenvironment. Chloroform levels were higher in restaurants than in other microenvironments, with a geometric mean of 1.1 microg/m3 (95% CI = 0.7, 1.8). The VOC concentrations found in stores and restaurants in this study are a potentially important source of exposure for sensitive individuals or people who work in these microenvironments.